Here we prepare codebooks for all input data from Exp 2.

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
library(pacman)
pacman::p_load(here,
               codebook,
               labelled,
               tidyverse)

ROI data

Primary analysis (localizers used to identify ROIs, extraction from VOE data)

Path: - input_data/ROI_analysis_outputs_Apr06_2023_convertVectors.csv

Note - the size of some datasets of this type was too big for the codebook package, so I filtered it down to just the data from the top 100 voxels.

ROI_data <- rio::import(here("input_data/ROI_analysis_outputs_Apr06_2023_convertVectors.csv")) %>%
  filter(top_voxel_selection_method == "100")


var_label(ROI_data) <- list(
        top_voxel_selection_method = "Size of extracted ROI (# voxels)", 
        ROI_name = "",
        ROI_category = "Psychology, physics, early visual, or multiple demand ROI",
        n_voxels_in_brainmasked_parcel = "N voxels in parcel",
        subjectID = "",
        contrast_for_topVoxel_selection = "Contrast basis for ROI selection",
        selection_contrast_task_name = "Task basis for ROI selection",
        extracted_copes_main_condition = "Which condition copes were extracted from",
        extracted_run_number = "Which run (1-4) copes were extracted from",
        mean_topVoxels_main_condition_copes = "Mean response from ROI",
        vector1_topVoxels_main_condition_copes = "Vector of responses in ROI"
)

metadata_list(ROI_data)

## $`@context`
## [1] "http://schema.org/"
## 
## $`@type`
## [1] "Dataset"
## 
## $variableMeasured
## $variableMeasured[[1]]
## $variableMeasured[[1]]$name
## [1] "top_voxel_selection_method"
## 
## $variableMeasured[[1]]$description
## [1] "Size of extracted ROI (# voxels)"
## 
## $variableMeasured[[1]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[2]]
## $variableMeasured[[2]]$name
## [1] "ROI_name"
## 
## $variableMeasured[[2]]$description
## [1] ""
## 
## $variableMeasured[[2]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[3]]
## $variableMeasured[[3]]$name
## [1] "ROI_category"
## 
## $variableMeasured[[3]]$description
## [1] "Psychology, physics, early visual, or multiple demand ROI"
## 
## $variableMeasured[[3]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[4]]
## $variableMeasured[[4]]$name
## [1] "n_voxels_in_brainmasked_parcel"
## 
## $variableMeasured[[4]]$description
## [1] "N voxels in parcel"
## 
## $variableMeasured[[4]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[5]]
## $variableMeasured[[5]]$name
## [1] "n_voxels_subject_specific_brainmasked_parcel"
## 
## $variableMeasured[[5]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[6]]
## $variableMeasured[[6]]$name
## [1] "subjectID"
## 
## $variableMeasured[[6]]$description
## [1] ""
## 
## $variableMeasured[[6]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[7]]
## $variableMeasured[[7]]$name
## [1] "contrast_for_topVoxel_selection"
## 
## $variableMeasured[[7]]$description
## [1] "Contrast basis for ROI selection"
## 
## $variableMeasured[[7]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[8]]
## $variableMeasured[[8]]$name
## [1] "selection_contrast_task_name"
## 
## $variableMeasured[[8]]$description
## [1] "Task basis for ROI selection"
## 
## $variableMeasured[[8]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[9]]
## $variableMeasured[[9]]$name
## [1] "extracted_copes_main_condition"
## 
## $variableMeasured[[9]]$description
## [1] "Which condition copes were extracted from"
## 
## $variableMeasured[[9]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[10]]
## $variableMeasured[[10]]$name
## [1] "extracted_run_number"
## 
## $variableMeasured[[10]]$description
## [1] "Which run (1-4) copes were extracted from"
## 
## $variableMeasured[[10]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[11]]
## $variableMeasured[[11]]$name
## [1] "mean_topVoxels_main_condition_copes"
## 
## $variableMeasured[[11]]$description
## [1] "Mean response from ROI"
## 
## $variableMeasured[[11]]$`@type`
## [1] "propertyValue"
## 
## 
## $variableMeasured[[12]]
## $variableMeasured[[12]]$name
## [1] "vector1_topVoxels_main_condition_copes"
## 
## $variableMeasured[[12]]$description
## [1] "Vector of responses in ROI"
## 
## $variableMeasured[[12]]$`@type`
## [1] "propertyValue"
## 
## 
## 
## $description
## [1] "\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                                         |label                                                     | n_missing|\n|:--------------------------------------------|:---------------------------------------------------------|---------:|\n|top_voxel_selection_method                   |Size of extracted ROI (# voxels)                          |         0|\n|ROI_name                                     |                                                          |         0|\n|ROI_category                                 |Psychology, physics, early visual, or multiple demand ROI |         0|\n|n_voxels_in_brainmasked_parcel               |N voxels in parcel                                        |         0|\n|n_voxels_subject_specific_brainmasked_parcel |NA                                                        |         0|\n|subjectID                                    |                                                          |         0|\n|contrast_for_topVoxel_selection              |Contrast basis for ROI selection                          |         0|\n|selection_contrast_task_name                 |Task basis for ROI selection                              |         0|\n|extracted_copes_main_condition               |Which condition copes were extracted from                 |         0|\n|extracted_run_number                         |Which run (1-4) copes were extracted from                 |         0|\n|mean_topVoxels_main_condition_copes          |Mean response from ROI                                    |         0|\n|vector1_topVoxels_main_condition_copes       |Vector of responses in ROI                                |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2)."

metadata(ROI_data)$name <- "ROI data, Exp 2"
metadata(ROI_data)$description <- "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al."
metadata(ROI_data)$datePublished <- "2023-08-21"
metadata(ROI_data)$creator <- list(
      "@type" = "Person",
      givenName = "Shari", familyName = "Liu",
      email = "shariliu@jhu.edu", 
      affiliation = list("@type" = "Organization",
        name = "Johns Hopkins University, Baltimore, MD, USA"))
metadata(ROI_data)$citation <- "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain."
metadata(ROI_data)$temporalCoverage <- "2023"

codebook(ROI_data)

## No missing values.

Metadata

Description

Dataset name: ROI data, Exp 2

ROI data from Exp2 in the manuscript entitled ‘Violations of physical and psychological expectations in the human adult brain’ by Liu et al.

Metadata for search engines

Temporal Coverage: 2023
Citation: Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.
Date published: 2023-08-21
Creator:

name	value
@type	Person
givenName	Shari
familyName	Liu
email	shariliu@jhu.edu
affiliation	Organization , Johns Hopkins University, Baltimore, MD, USA

x
top_voxel_selection_method
ROI_name
ROI_category
n_voxels_in_brainmasked_parcel
n_voxels_subject_specific_brainmasked_parcel
subjectID
contrast_for_topVoxel_selection
selection_contrast_task_name
extracted_copes_main_condition
extracted_run_number
mean_topVoxels_main_condition_copes
vector1_topVoxels_main_condition_copes

#Variables

top_voxel_selection_method

Size of extracted ROI (# voxels)

Distribution

Distribution of values for top_voxel_selection_method

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
top_voxel_selection_method	Size of extracted ROI (# voxels)	numeric	0	1	100	100	100	100	0	▁▁▇▁▁

ROI_name

Distribution

Distribution of values for ROI_name

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ROI_name		character	0	1	48	0	34	79	0

ROI_category

Psychology, physics, early visual, or multiple demand ROI

Distribution

Distribution of values for ROI_category

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ROI_category	Psychology, physics, early visual, or multiple demand ROI	character	0	1	4	0	2	12	0

n_voxels_in_brainmasked_parcel

N voxels in parcel

Distribution

Distribution of values for n_voxels_in_brainmasked_parcel

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
n_voxels_in_brainmasked_parcel	N voxels in parcel	numeric	0	1	347	1736	6411	2279.625	1431.958	▇▆▆▂▁

n_voxels_subject_specific_brainmasked_parcel

Distribution

Distribution of values for n_voxels_subject_specific_brainmasked_parcel

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
n_voxels_subject_specific_brainmasked_parcel	numeric	0	1	347	1731	6411	2278.638	1431.775	▇▆▆▂▁	NA

subjectID

Distribution

Distribution of values for subjectID

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
subjectID		character	0	1	32	0	15	16	0

contrast_for_topVoxel_selection

Contrast basis for ROI selection

Distribution

Distribution of values for contrast_for_topVoxel_selection

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contrast_for_topVoxel_selection	Contrast basis for ROI selection	character	0	1	5	0	11	22	0

selection_contrast_task_name

Task basis for ROI selection

Distribution

Distribution of values for selection_contrast_task_name

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
selection_contrast_task_name	Task basis for ROI selection	character	0	1	3	0	4	6	0

extracted_copes_main_condition

Which condition copes were extracted from

Distribution

Distribution of values for extracted_copes_main_condition

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
extracted_copes_main_condition	Which condition copes were extracted from	character	0	1	9	0	8	16	0

extracted_run_number

Which run (1-4) copes were extracted from

Distribution

Distribution of values for extracted_run_number

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
extracted_run_number	Which run (1-4) copes were extracted from	character	0	1	10	0	4	17	0

mean_topVoxels_main_condition_copes

Mean response from ROI

Distribution

Distribution of values for mean_topVoxels_main_condition_copes

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
mean_topVoxels_main_condition_copes	Mean response from ROI	numeric	0	1	-21	2	43	2.808981	3.584287	▁▇▂▁▁

vector1_topVoxels_main_condition_copes

Vector of responses in ROI

Distribution

Distribution of values for vector1_topVoxels_main_condition_copes

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
vector1_topVoxels_main_condition_copes	Vector of responses in ROI	character	0	1	121527	0	1170	1434	0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "ROI data, Exp 2",
  "description": "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                                         |label                                                     | n_missing|\n|:--------------------------------------------|:---------------------------------------------------------|---------:|\n|top_voxel_selection_method                   |Size of extracted ROI (# voxels)                          |         0|\n|ROI_name                                     |                                                          |         0|\n|ROI_category                                 |Psychology, physics, early visual, or multiple demand ROI |         0|\n|n_voxels_in_brainmasked_parcel               |N voxels in parcel                                        |         0|\n|n_voxels_subject_specific_brainmasked_parcel |NA                                                        |         0|\n|subjectID                                    |                                                          |         0|\n|contrast_for_topVoxel_selection              |Contrast basis for ROI selection                          |         0|\n|selection_contrast_task_name                 |Task basis for ROI selection                              |         0|\n|extracted_copes_main_condition               |Which condition copes were extracted from                 |         0|\n|extracted_run_number                         |Which run (1-4) copes were extracted from                 |         0|\n|mean_topVoxels_main_condition_copes          |Mean response from ROI                                    |         0|\n|vector1_topVoxels_main_condition_copes       |Vector of responses in ROI                                |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "datePublished": "2023-08-21",
  "creator": {
    "@type": "Person",
    "givenName": "Shari",
    "familyName": "Liu",
    "email": "shariliu@jhu.edu",
    "affiliation": {
      "@type": "Organization",
      "name": "Johns Hopkins University, Baltimore, MD, USA"
    }
  },
  "citation": "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.",
  "temporalCoverage": "2023",
  "keywords": ["top_voxel_selection_method", "ROI_name", "ROI_category", "n_voxels_in_brainmasked_parcel", "n_voxels_subject_specific_brainmasked_parcel", "subjectID", "contrast_for_topVoxel_selection", "selection_contrast_task_name", "extracted_copes_main_condition", "extracted_run_number", "mean_topVoxels_main_condition_copes", "vector1_topVoxels_main_condition_copes"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "top_voxel_selection_method",
      "description": "Size of extracted ROI (# voxels)",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_name",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_category",
      "description": "Psychology, physics, early visual, or multiple demand ROI",
      "@type": "propertyValue"
    },
    {
      "name": "n_voxels_in_brainmasked_parcel",
      "description": "N voxels in parcel",
      "@type": "propertyValue"
    },
    {
      "name": "n_voxels_subject_specific_brainmasked_parcel",
      "@type": "propertyValue"
    },
    {
      "name": "subjectID",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "contrast_for_topVoxel_selection",
      "description": "Contrast basis for ROI selection",
      "@type": "propertyValue"
    },
    {
      "name": "selection_contrast_task_name",
      "description": "Task basis for ROI selection",
      "@type": "propertyValue"
    },
    {
      "name": "extracted_copes_main_condition",
      "description": "Which condition copes were extracted from",
      "@type": "propertyValue"
    },
    {
      "name": "extracted_run_number",
      "description": "Which run (1-4) copes were extracted from",
      "@type": "propertyValue"
    },
    {
      "name": "mean_topVoxels_main_condition_copes",
      "description": "Mean response from ROI",
      "@type": "propertyValue"
    },
    {
      "name": "vector1_topVoxels_main_condition_copes",
      "description": "Vector of responses in ROI",
      "@type": "propertyValue"
    }
  ]
}`

Single betas (one magnitude extracted per event)

Path: - input_data/ROI_analysis_singlebetas_outputs_Apr11_2023_convertVectors.csv

Note - the size of some datasets of this type was too big for the codebook package, so I filtered it down to just the data from the top 100 voxels.

single_betas_data <- rio::import(here("input_data/ROI_analysis_singlebetas_outputs_Apr11_2023_convertVectors.csv")) %>%
  filter(top_voxel_selection_method == "100")


var_label(single_betas_data) <- list(
        top_voxel_selection_method = "Size of extracted ROI (# voxels)", 
        ROI_name = "",
        ROI_category = "Psychology, physics, early visual, or multiple demand ROI",
        n_voxels_in_brainmasked_parcel = "N voxels in parcel",
        subjectID = "",
        contrast_for_topVoxel_selection = "Contrast basis for ROI selection",
        selection_contrast_task_name = "Task basis for ROI selection",
        extracted_copes_main_condition = "Which condition copes were extracted from",
        extracted_run_number = "Which run (1-4) copes were extracted from",
        mean_topVoxels_main_condition_copes = "Mean response from ROI",
        vector1_topVoxels_main_condition_copes = "Vector of responses in ROI"
)


metadata(single_betas_data)$name <- "ROI data, Exp 2"
metadata(single_betas_data)$description <- "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al."
metadata(single_betas_data)$datePublished <- "2023-08-21"
metadata(single_betas_data)$creator <- list(
      "@type" = "Person",
      givenName = "Shari", familyName = "Liu",
      email = "shariliu@jhu.edu", 
      affiliation = list("@type" = "Organization",
        name = "Johns Hopkins University, Baltimore, MD, USA"))
metadata(single_betas_data)$citation <- "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain."
metadata(single_betas_data)$temporalCoverage <- "2023"

codebook(single_betas_data)

Metadata

Description

Dataset name: ROI data, Exp 2

ROI data from Exp2 in the manuscript entitled ‘Violations of physical and psychological expectations in the human adult brain’ by Liu et al.

Metadata for search engines

Temporal Coverage: 2023
Citation: Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.
Date published: 2023-08-21
Creator:

name	value
@type	Person
givenName	Shari
familyName	Liu
email	shariliu@jhu.edu
affiliation	Organization , Johns Hopkins University, Baltimore, MD, USA

x
top_voxel_selection_method
ROI_name
ROI_category
n_voxels_in_brainmasked_parcel
n_voxels_subject_specific_brainmasked_parcel
subjectID
contrast_for_topVoxel_selection
selection_contrast_task_name
extracted_copes_main_condition
event_n_within_run
extracted_run_number
mean_topVoxels_main_condition_copes
vector1_topVoxels_main_condition_copes

#Variables

top_voxel_selection_method

Size of extracted ROI (# voxels)

Distribution

Distribution of values for top_voxel_selection_method

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
top_voxel_selection_method	Size of extracted ROI (# voxels)	numeric	0	1	100	100	100	100	0	▁▁▇▁▁

ROI_name

Distribution

Distribution of values for ROI_name

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ROI_name		character	0	1	48	0	34	79	0

ROI_category

Psychology, physics, early visual, or multiple demand ROI

Distribution

Distribution of values for ROI_category

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ROI_category	Psychology, physics, early visual, or multiple demand ROI	character	0	1	3	0	2	12	0

n_voxels_in_brainmasked_parcel

N voxels in parcel

Distribution

## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed

## No non-missing values to show.

201168 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	count
n_voxels_in_brainmasked_parcel	N voxels in parcel	logical	201168	0	:

n_voxels_subject_specific_brainmasked_parcel

Distribution

## Error in if (stats::median(table(x)) == 1) {: missing value where TRUE/FALSE needed

## No non-missing values to show.

201168 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	count	label
n_voxels_subject_specific_brainmasked_parcel	logical	201168	0	:	NA

subjectID

Distribution

Distribution of values for subjectID

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
subjectID		character	0	1	32	0	15	16	0

contrast_for_topVoxel_selection

Contrast basis for ROI selection

Distribution

Distribution of values for contrast_for_topVoxel_selection

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contrast_for_topVoxel_selection	Contrast basis for ROI selection	character	0	1	5	0	20	31	0

selection_contrast_task_name

Task basis for ROI selection

Distribution

Distribution of values for selection_contrast_task_name

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
selection_contrast_task_name	Task basis for ROI selection	character	0	1	3	0	9	11	0

extracted_copes_main_condition

Which condition copes were extracted from

Distribution

Distribution of values for extracted_copes_main_condition

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
extracted_copes_main_condition	Which condition copes were extracted from	character	0	1	85	0	5	43	0

event_n_within_run

Distribution

Distribution of values for event_n_within_run

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
event_n_within_run	numeric	0	1	1	17	33	17	9.521928	▇▇▇▇▇	NA

extracted_run_number

Which run (1-4) copes were extracted from

Distribution

Distribution of values for extracted_run_number

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
extracted_run_number	Which run (1-4) copes were extracted from	character	0	1	4	0	4	4	0

mean_topVoxels_main_condition_copes

Mean response from ROI

Distribution

Distribution of values for mean_topVoxels_main_condition_copes

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
mean_topVoxels_main_condition_copes	Mean response from ROI	numeric	0	1	-40	2	43	2.774496	4.294106	▁▁▇▁▁

vector1_topVoxels_main_condition_copes

Vector of responses in ROI

Distribution

Distribution of values for vector1_topVoxels_main_condition_copes

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
vector1_topVoxels_main_condition_copes	Vector of responses in ROI	character	0	1	197868	0	1170	1392	0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "ROI data, Exp 2",
  "description": "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                                         |label                                                     | n_missing|\n|:--------------------------------------------|:---------------------------------------------------------|---------:|\n|top_voxel_selection_method                   |Size of extracted ROI (# voxels)                          |         0|\n|ROI_name                                     |                                                          |         0|\n|ROI_category                                 |Psychology, physics, early visual, or multiple demand ROI |         0|\n|n_voxels_in_brainmasked_parcel               |N voxels in parcel                                        |    201168|\n|n_voxels_subject_specific_brainmasked_parcel |NA                                                        |    201168|\n|subjectID                                    |                                                          |         0|\n|contrast_for_topVoxel_selection              |Contrast basis for ROI selection                          |         0|\n|selection_contrast_task_name                 |Task basis for ROI selection                              |         0|\n|extracted_copes_main_condition               |Which condition copes were extracted from                 |         0|\n|event_n_within_run                           |NA                                                        |         0|\n|extracted_run_number                         |Which run (1-4) copes were extracted from                 |         0|\n|mean_topVoxels_main_condition_copes          |Mean response from ROI                                    |         0|\n|vector1_topVoxels_main_condition_copes       |Vector of responses in ROI                                |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "datePublished": "2023-08-21",
  "creator": {
    "@type": "Person",
    "givenName": "Shari",
    "familyName": "Liu",
    "email": "shariliu@jhu.edu",
    "affiliation": {
      "@type": "Organization",
      "name": "Johns Hopkins University, Baltimore, MD, USA"
    }
  },
  "citation": "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.",
  "temporalCoverage": "2023",
  "keywords": ["top_voxel_selection_method", "ROI_name", "ROI_category", "n_voxels_in_brainmasked_parcel", "n_voxels_subject_specific_brainmasked_parcel", "subjectID", "contrast_for_topVoxel_selection", "selection_contrast_task_name", "extracted_copes_main_condition", "event_n_within_run", "extracted_run_number", "mean_topVoxels_main_condition_copes", "vector1_topVoxels_main_condition_copes"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "top_voxel_selection_method",
      "description": "Size of extracted ROI (# voxels)",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_name",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_category",
      "description": "Psychology, physics, early visual, or multiple demand ROI",
      "@type": "propertyValue"
    },
    {
      "name": "n_voxels_in_brainmasked_parcel",
      "description": "N voxels in parcel",
      "@type": "propertyValue"
    },
    {
      "name": "n_voxels_subject_specific_brainmasked_parcel",
      "@type": "propertyValue"
    },
    {
      "name": "subjectID",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "contrast_for_topVoxel_selection",
      "description": "Contrast basis for ROI selection",
      "@type": "propertyValue"
    },
    {
      "name": "selection_contrast_task_name",
      "description": "Task basis for ROI selection",
      "@type": "propertyValue"
    },
    {
      "name": "extracted_copes_main_condition",
      "description": "Which condition copes were extracted from",
      "@type": "propertyValue"
    },
    {
      "name": "event_n_within_run",
      "@type": "propertyValue"
    },
    {
      "name": "extracted_run_number",
      "description": "Which run (1-4) copes were extracted from",
      "@type": "propertyValue"
    },
    {
      "name": "mean_topVoxels_main_condition_copes",
      "description": "Mean response from ROI",
      "@type": "propertyValue"
    },
    {
      "name": "vector1_topVoxels_main_condition_copes",
      "description": "Vector of responses in ROI",
      "@type": "propertyValue"
    }
  ]
}`

Secondary analysis (VOE runs 3-4 used to identify ROIs for all regions)

Path: - input_data/ROI_analysis_VOErun34extract_outputs_Apr08_2023_convertVectors.csv

Note - the size of some datasets of this type was too big for the codebook package, so I filtered it down to just the data from the top 100 voxels.

ROI_VOE_select_data <- rio::import(here("input_data/ROI_analysis_VOErun34extract_outputs_Apr08_2023_convertVectors.csv")) %>%
  filter(top_voxel_selection_method == "100")


var_label(ROI_VOE_select_data) <- list(
        top_voxel_selection_method = "Size of extracted ROI (# voxels)", 
        ROI_name = "",
        ROI_category = "Psychology, physics, early visual, or multiple demand ROI",
        n_voxels_in_brainmasked_parcel = "N voxels in parcel",
        subjectID = "",
        contrast_for_topVoxel_selection = "Contrast basis for ROI selection",
        selection_contrast_task_name = "Task basis for ROI selection",
        extracted_copes_main_condition = "Which condition copes were extracted from",
        extracted_run_number = "Which run (1-4) copes were extracted from",
        mean_topVoxels_main_condition_copes = "Mean response from ROI",
        vector1_topVoxels_main_condition_copes = "Vector of responses in ROI"
)


metadata(ROI_VOE_select_data)$name <- "ROI data, Exp 2"
metadata(ROI_VOE_select_data)$description <- "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al."
metadata(ROI_VOE_select_data)$datePublished <- "2023-08-21"
metadata(ROI_VOE_select_data)$creator <- list(
      "@type" = "Person",
      givenName = "Shari", familyName = "Liu",
      email = "shariliu@jhu.edu", 
      affiliation = list("@type" = "Organization",
        name = "Johns Hopkins University, Baltimore, MD, USA"))
metadata(ROI_VOE_select_data)$citation <- "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain."
metadata(ROI_VOE_select_data)$temporalCoverage <- "2023"

codebook(ROI_VOE_select_data)

## No missing values.

Metadata

Description

Dataset name: ROI data, Exp 2

ROI data from Exp2 in the manuscript entitled ‘Violations of physical and psychological expectations in the human adult brain’ by Liu et al.

Metadata for search engines

Temporal Coverage: 2023
Citation: Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.
Date published: 2023-08-21
Creator:

name	value
@type	Person
givenName	Shari
familyName	Liu
email	shariliu@jhu.edu
affiliation	Organization , Johns Hopkins University, Baltimore, MD, USA

x
top_voxel_selection_method
ROI_name
ROI_category
n_voxels_in_brainmasked_parcel
n_voxels_subject_specific_brainmasked_parcel
subjectID
contrast_for_topVoxel_selection
selection_contrast_task_name
extracted_copes_main_condition
extracted_run_number
mean_topVoxels_main_condition_copes
vector1_topVoxels_main_condition_copes

#Variables

top_voxel_selection_method

Size of extracted ROI (# voxels)

Distribution

Distribution of values for top_voxel_selection_method

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
top_voxel_selection_method	Size of extracted ROI (# voxels)	numeric	0	1	100	100	100	100	0	▁▁▇▁▁

ROI_name

Distribution

Distribution of values for ROI_name

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ROI_name		character	0	1	48	0	34	79	0

ROI_category

Psychology, physics, early visual, or multiple demand ROI

Distribution

Distribution of values for ROI_category

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ROI_category	Psychology, physics, early visual, or multiple demand ROI	character	0	1	4	0	2	12	0

n_voxels_in_brainmasked_parcel

N voxels in parcel

Distribution

Distribution of values for n_voxels_in_brainmasked_parcel

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
n_voxels_in_brainmasked_parcel	N voxels in parcel	numeric	0	1	347	1736	6411	2279.625	1431.978	▇▆▆▂▁

n_voxels_subject_specific_brainmasked_parcel

Distribution

Distribution of values for n_voxels_subject_specific_brainmasked_parcel

0 missing values.

Summary statistics

name	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist	label
n_voxels_subject_specific_brainmasked_parcel	numeric	0	1	347	1731	6411	2278.645	1431.796	▇▆▆▂▁	NA

subjectID

Distribution

Distribution of values for subjectID

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
subjectID		character	0	1	32	0	15	16	0

contrast_for_topVoxel_selection

Contrast basis for ROI selection

Distribution

Distribution of values for contrast_for_topVoxel_selection

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
contrast_for_topVoxel_selection	Contrast basis for ROI selection	character	0	1	3	0	19	28	0

selection_contrast_task_name

Task basis for ROI selection

Distribution

Distribution of values for selection_contrast_task_name

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
selection_contrast_task_name	Task basis for ROI selection	character	0	1	1	0	3	3	0

extracted_copes_main_condition

Which condition copes were extracted from

Distribution

Distribution of values for extracted_copes_main_condition

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
extracted_copes_main_condition	Which condition copes were extracted from	character	0	1	9	0	8	16	0

extracted_run_number

Which run (1-4) copes were extracted from

Distribution

Distribution of values for extracted_run_number

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
extracted_run_number	Which run (1-4) copes were extracted from	character	0	1	2	0	4	4	0

mean_topVoxels_main_condition_copes

Mean response from ROI

Distribution

Distribution of values for mean_topVoxels_main_condition_copes

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
mean_topVoxels_main_condition_copes	Mean response from ROI	numeric	0	1	-14	0.9	28	1.519659	2.727292	▁▇▂▁▁

vector1_topVoxels_main_condition_copes

Vector of responses in ROI

Distribution

Distribution of values for vector1_topVoxels_main_condition_copes

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
vector1_topVoxels_main_condition_copes	Vector of responses in ROI	character	0	1	27396	0	1173	1408	0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "ROI data, Exp 2",
  "description": "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                                         |label                                                     | n_missing|\n|:--------------------------------------------|:---------------------------------------------------------|---------:|\n|top_voxel_selection_method                   |Size of extracted ROI (# voxels)                          |         0|\n|ROI_name                                     |                                                          |         0|\n|ROI_category                                 |Psychology, physics, early visual, or multiple demand ROI |         0|\n|n_voxels_in_brainmasked_parcel               |N voxels in parcel                                        |         0|\n|n_voxels_subject_specific_brainmasked_parcel |NA                                                        |         0|\n|subjectID                                    |                                                          |         0|\n|contrast_for_topVoxel_selection              |Contrast basis for ROI selection                          |         0|\n|selection_contrast_task_name                 |Task basis for ROI selection                              |         0|\n|extracted_copes_main_condition               |Which condition copes were extracted from                 |         0|\n|extracted_run_number                         |Which run (1-4) copes were extracted from                 |         0|\n|mean_topVoxels_main_condition_copes          |Mean response from ROI                                    |         0|\n|vector1_topVoxels_main_condition_copes       |Vector of responses in ROI                                |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "datePublished": "2023-08-21",
  "creator": {
    "@type": "Person",
    "givenName": "Shari",
    "familyName": "Liu",
    "email": "shariliu@jhu.edu",
    "affiliation": {
      "@type": "Organization",
      "name": "Johns Hopkins University, Baltimore, MD, USA"
    }
  },
  "citation": "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.",
  "temporalCoverage": "2023",
  "keywords": ["top_voxel_selection_method", "ROI_name", "ROI_category", "n_voxels_in_brainmasked_parcel", "n_voxels_subject_specific_brainmasked_parcel", "subjectID", "contrast_for_topVoxel_selection", "selection_contrast_task_name", "extracted_copes_main_condition", "extracted_run_number", "mean_topVoxels_main_condition_copes", "vector1_topVoxels_main_condition_copes"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "top_voxel_selection_method",
      "description": "Size of extracted ROI (# voxels)",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_name",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_category",
      "description": "Psychology, physics, early visual, or multiple demand ROI",
      "@type": "propertyValue"
    },
    {
      "name": "n_voxels_in_brainmasked_parcel",
      "description": "N voxels in parcel",
      "@type": "propertyValue"
    },
    {
      "name": "n_voxels_subject_specific_brainmasked_parcel",
      "@type": "propertyValue"
    },
    {
      "name": "subjectID",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "contrast_for_topVoxel_selection",
      "description": "Contrast basis for ROI selection",
      "@type": "propertyValue"
    },
    {
      "name": "selection_contrast_task_name",
      "description": "Task basis for ROI selection",
      "@type": "propertyValue"
    },
    {
      "name": "extracted_copes_main_condition",
      "description": "Which condition copes were extracted from",
      "@type": "propertyValue"
    },
    {
      "name": "extracted_run_number",
      "description": "Which run (1-4) copes were extracted from",
      "@type": "propertyValue"
    },
    {
      "name": "mean_topVoxels_main_condition_copes",
      "description": "Mean response from ROI",
      "@type": "propertyValue"
    },
    {
      "name": "vector1_topVoxels_main_condition_copes",
      "description": "Vector of responses in ROI",
      "@type": "propertyValue"
    }
  ]
}`

Data about ROI overlap

Path: - input_data/ROI_overlap_outputs_Apr19_2023.csv

This dataset computes Dice’s Coefficient between all ROIs per subject.

overlap_data <- rio::import(here("input_data/ROI_overlap_outputs_Apr19_2023.csv")) 

var_label(overlap_data) <- list(
        participantID = "Subject identifier", 
        ROI_1 = "Name of ROI 1",
        roi_2 = "Name of ROI 2",
        overlap_dices_coef = "Dice's Coefficient between these two ROIs"
)


metadata(overlap_data)$name <- "ROI data, Exp 2"
metadata(overlap_data)$description <- "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al."
metadata(overlap_data)$datePublished <- "2023-08-21"
metadata(overlap_data)$creator <- list(
      "@type" = "Person",
      givenName = "Shari", familyName = "Liu",
      email = "shariliu@jhu.edu", 
      affiliation = list("@type" = "Organization",
        name = "Johns Hopkins University, Baltimore, MD, USA"))
metadata(overlap_data)$citation <- "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain."
metadata(overlap_data)$temporalCoverage <- "2023"

codebook(overlap_data)

## No missing values.

Metadata

Description

Dataset name: ROI data, Exp 2

ROI data from Exp2 in the manuscript entitled ‘Violations of physical and psychological expectations in the human adult brain’ by Liu et al.

Metadata for search engines

Temporal Coverage: 2023
Citation: Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.
Date published: 2023-08-21
Creator:

name	value
@type	Person
givenName	Shari
familyName	Liu
email	shariliu@jhu.edu
affiliation	Organization , Johns Hopkins University, Baltimore, MD, USA

x
participantID
ROI_1
roi_2
overlap_dices_coef

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
overlap_dices_coef	Dice’s Coefficient between these two ROIs	numeric	0	1	0	0	0.66	0.001275	0.0175192	▇▁▁▁▁

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "ROI data, Exp 2",
  "description": "ROI data from Exp2 in the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name               |label                                     | n_missing|\n|:------------------|:-----------------------------------------|---------:|\n|participantID      |Subject identifier                        |         0|\n|ROI_1              |Name of ROI 1                             |         0|\n|roi_2              |Name of ROI 2                             |         0|\n|overlap_dices_coef |Dice's Coefficient between these two ROIs |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "datePublished": "2023-08-21",
  "creator": {
    "@type": "Person",
    "givenName": "Shari",
    "familyName": "Liu",
    "email": "shariliu@jhu.edu",
    "affiliation": {
      "@type": "Organization",
      "name": "Johns Hopkins University, Baltimore, MD, USA"
    }
  },
  "citation": "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.",
  "temporalCoverage": "2023",
  "keywords": ["participantID", "ROI_1", "roi_2", "overlap_dices_coef"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "participantID",
      "description": "Subject identifier",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_1",
      "description": "Name of ROI 1",
      "@type": "propertyValue"
    },
    {
      "name": "roi_2",
      "description": "Name of ROI 2",
      "@type": "propertyValue"
    },
    {
      "name": "overlap_dices_coef",
      "description": "Dice's Coefficient between these two ROIs",
      "@type": "propertyValue"
    }
  ]
}`

Subject info

Path: input_data/exp2_study_subjects.csv

subj_info <- rio::import(here("input_data/exp2_study_subjects.csv"))


var_label(subj_info) <- list(
        experiment = "", 
        include = "Include subject in analysis?",
        exclude_why = "If exclude, why?",
        subjectID = "",
        fixation_position = "Did fixation come before or after attention check?",
        gender = "Self-reported gender",
        handed = "Left or right handed",
        age = "Age in years at time of scan",
        data_sharing = "Consented to sharing de-faced images on OpenNeuro?"
)


metadata(subj_info)$name <- "Subject info, Experiment 2"
metadata(subj_info)$description <- "Subject information for the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al."
metadata(subj_info)$datePublished <- "2023-08-21"
metadata(subj_info)$creator <- list(
      "@type" = "Person",
      givenName = "Shari", familyName = "Liu",
      email = "shariliu@jhu.edu", 
      affiliation = list("@type" = "Organization",
        name = "Johns Hopkins University, Baltimore, MD, USA"))
metadata(ROI_data)$citation <- "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain."
metadata(subj_info)$temporalCoverage <- "2023"

codebook(subj_info)

## No missing values.

Metadata

Description

Dataset name: Subject info, Experiment 2

Subject information for the manuscript entitled ‘Violations of physical and psychological expectations in the human adult brain’ by Liu et al.

Metadata for search engines

Temporal Coverage: 2023
Date published: 2023-08-21
Creator:

name	value
@type	Person
givenName	Shari
familyName	Liu
email	shariliu@jhu.edu
affiliation	Organization , Johns Hopkins University, Baltimore, MD, USA

x
experiment
include
exclude_why
subjectID
fixation_position
gender
handed
age
data_sharing

#Variables

experiment

Distribution

Distribution of values for experiment

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
experiment		character	0	1	1	0	6	6	0

include

Include subject in analysis?

Distribution

Distribution of values for include

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
include	Include subject in analysis?	numeric	0	1	0	1	1	0.969697	0.1740777	▁▁▁▁▇

exclude_why

If exclude, why?

Distribution

Distribution of values for exclude_why

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
exclude_why	If exclude, why?	character	0	1	2	32	0	14	0

subjectID

Distribution

Distribution of values for subjectID

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
subjectID		character	0	1	33	0	15	16	0

fixation_position

Did fixation come before or after attention check?

Distribution

Distribution of values for fixation_position

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
fixation_position	Did fixation come before or after attention check?	character	0	1	2	0	5	6	0

gender

Self-reported gender

Distribution

Distribution of values for gender

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
gender	Self-reported gender	character	0	1	2	0	1	1	0

handed

Left or right handed

Distribution

Distribution of values for handed

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
handed	Left or right handed	character	0	1	2	0	4	5	0

age

Age in years at time of scan

Distribution

Distribution of values for age

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
age	Age in years at time of scan	numeric	0	1	18	24	45	26.21212	7.753054	▇▃▂▂▂

data_sharing

Consented to sharing de-faced images on OpenNeuro?

Distribution

Distribution of values for data_sharing

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
data_sharing	Consented to sharing de-faced images on OpenNeuro?	numeric	0	1	0	1	1	0.9090909	0.2919371	▁▁▁▁▇

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Subject info, Experiment 2",
  "description": "Subject information for the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name              |label                                              | n_missing|\n|:-----------------|:--------------------------------------------------|---------:|\n|experiment        |                                                   |         0|\n|include           |Include subject in analysis?                       |         0|\n|exclude_why       |If exclude, why?                                   |         0|\n|subjectID         |                                                   |         0|\n|fixation_position |Did fixation come before or after attention check? |         0|\n|gender            |Self-reported gender                               |         0|\n|handed            |Left or right handed                               |         0|\n|age               |Age in years at time of scan                       |         0|\n|data_sharing      |Consented to sharing de-faced images on OpenNeuro? |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "datePublished": "2023-08-21",
  "creator": {
    "@type": "Person",
    "givenName": "Shari",
    "familyName": "Liu",
    "email": "shariliu@jhu.edu",
    "affiliation": {
      "@type": "Organization",
      "name": "Johns Hopkins University, Baltimore, MD, USA"
    }
  },
  "temporalCoverage": "2023",
  "keywords": ["experiment", "include", "exclude_why", "subjectID", "fixation_position", "gender", "handed", "age", "data_sharing"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "experiment",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "include",
      "description": "Include subject in analysis?",
      "@type": "propertyValue"
    },
    {
      "name": "exclude_why",
      "description": "If exclude, why?",
      "@type": "propertyValue"
    },
    {
      "name": "subjectID",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "fixation_position",
      "description": "Did fixation come before or after attention check?",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Self-reported gender",
      "@type": "propertyValue"
    },
    {
      "name": "handed",
      "description": "Left or right handed",
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Age in years at time of scan",
      "@type": "propertyValue"
    },
    {
      "name": "data_sharing",
      "description": "Consented to sharing de-faced images on OpenNeuro?",
      "@type": "propertyValue"
    }
  ]
}`

Region info

Path: input_data/manyregions_info.csv

region_info <- rio::import(here("input_data/manyregions_info.csv"))

var_label(region_info) <- list(
        ROI_name = "Original ROI name that matches file names of ROI parcels", 
        ROI_name_final = "Simplified ROI name",
        ROI_category = "Psychology physics early visual or MD",
        bilateral = "Is this a bilateral ROI?",
        focal_region = "Focal region in analyses?",
        manyregions_region = "Included in regions involving many ROIs?",
        old_ROI = "Old ROI that was generated due to mistake in analysis? Included for transparency",
        parcel_overlaps_with = "All the other ROIs this ROI overlaps with"
)

metadata(region_info)$name <- "Region info, Experiment 2"
metadata(region_info)$description <- "Region information for the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al."
metadata(region_info)$datePublished <- "2023-08-21"
metadata(region_info)$creator <- list(
      "@type" = "Person",
      givenName = "Shari", familyName = "Liu",
      email = "shariliu@jhu.edu", 
      affiliation = list("@type" = "Organization",
        name = "Johns Hopkins University, Baltimore, MD, USA"))
metadata(region_info)$citation <- "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain."
metadata(region_info)$temporalCoverage <- "2023"

codebook(region_info)

## No missing values.

Metadata

Description

Dataset name: Region info, Experiment 2

Region information for the manuscript entitled ‘Violations of physical and psychological expectations in the human adult brain’ by Liu et al.

Metadata for search engines

Temporal Coverage: 2023
Citation: Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.
Date published: 2023-08-21
Creator:

name	value
@type	Person
givenName	Shari
familyName	Liu
email	shariliu@jhu.edu
affiliation	Organization , Johns Hopkins University, Baltimore, MD, USA

x
ROI_name
ROI_name_final
ROI_category
bilateral
focal_region
manyregions_region
old_ROI
parcel_overlaps_with

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
parcel_overlaps_with	All the other ROIs this ROI overlaps with	character	0	1	32	9	0	107	0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Region info, Experiment 2",
  "description": "Region information for the manuscript entitled 'Violations of physical and psychological expectations in the human adult brain' by Liu et al.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                 |label                                                                            | n_missing|\n|:--------------------|:--------------------------------------------------------------------------------|---------:|\n|ROI_name             |Original ROI name that matches file names of ROI parcels                         |         0|\n|ROI_name_final       |Simplified ROI name                                                              |         0|\n|ROI_category         |Psychology physics early visual or MD                                            |         0|\n|bilateral            |Is this a bilateral ROI?                                                         |         0|\n|focal_region         |Focal region in analyses?                                                        |         0|\n|manyregions_region   |Included in regions involving many ROIs?                                         |         0|\n|old_ROI              |Old ROI that was generated due to mistake in analysis? Included for transparency |         0|\n|parcel_overlaps_with |All the other ROIs this ROI overlaps with                                        |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "datePublished": "2023-08-21",
  "creator": {
    "@type": "Person",
    "givenName": "Shari",
    "familyName": "Liu",
    "email": "shariliu@jhu.edu",
    "affiliation": {
      "@type": "Organization",
      "name": "Johns Hopkins University, Baltimore, MD, USA"
    }
  },
  "citation": "Liu, S., Lydic, K., Mei, L., & Saxe, R. (2023, preprint). Violations of physical and psychological expectations in the human adult brain.",
  "temporalCoverage": "2023",
  "keywords": ["ROI_name", "ROI_name_final", "ROI_category", "bilateral", "focal_region", "manyregions_region", "old_ROI", "parcel_overlaps_with"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "ROI_name",
      "description": "Original ROI name that matches file names of ROI parcels",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_name_final",
      "description": "Simplified ROI name",
      "@type": "propertyValue"
    },
    {
      "name": "ROI_category",
      "description": "Psychology physics early visual or MD",
      "@type": "propertyValue"
    },
    {
      "name": "bilateral",
      "description": "Is this a bilateral ROI?",
      "@type": "propertyValue"
    },
    {
      "name": "focal_region",
      "description": "Focal region in analyses?",
      "@type": "propertyValue"
    },
    {
      "name": "manyregions_region",
      "description": "Included in regions involving many ROIs?",
      "@type": "propertyValue"
    },
    {
      "name": "old_ROI",
      "description": "Old ROI that was generated due to mistake in analysis? Included for transparency",
      "@type": "propertyValue"
    },
    {
      "name": "parcel_overlaps_with",
      "description": "All the other ROIs this ROI overlaps with",
      "@type": "propertyValue"
    }
  ]
}`

4_Data_Codebooks

ROI data

Primary analysis (localizers used to identify ROIs, extraction from VOE data)

Metadata

Description

top_voxel_selection_method

Distribution

Summary statistics

ROI_name

Distribution

Summary statistics

ROI_category

Distribution

Summary statistics

n_voxels_in_brainmasked_parcel

Distribution

Summary statistics

n_voxels_subject_specific_brainmasked_parcel

Distribution

Summary statistics

subjectID

Distribution

Summary statistics

contrast_for_topVoxel_selection

Distribution

Summary statistics

selection_contrast_task_name

Distribution

Summary statistics

extracted_copes_main_condition

Distribution

Summary statistics

extracted_run_number

Distribution

Summary statistics

mean_topVoxels_main_condition_copes

Distribution

Summary statistics

vector1_topVoxels_main_condition_copes

Distribution

Summary statistics

Missingness report

Codebook table

Single betas (one magnitude extracted per event)

Metadata

Description

top_voxel_selection_method

Distribution

Summary statistics

ROI_name

Distribution

Summary statistics

ROI_category

Distribution

Summary statistics

n_voxels_in_brainmasked_parcel

Distribution

Summary statistics

n_voxels_subject_specific_brainmasked_parcel

Distribution

Summary statistics

subjectID

Distribution

Summary statistics

contrast_for_topVoxel_selection

Distribution

Summary statistics

selection_contrast_task_name

Distribution

Summary statistics

extracted_copes_main_condition

Distribution

Summary statistics

event_n_within_run

Distribution

Summary statistics

extracted_run_number

Distribution

Summary statistics

mean_topVoxels_main_condition_copes